Optimizing solar projects with NSY, LCOE and IRR metrics in mind, and how solar software can help

As a result of the ITC extension, a flurry of utility-scale solar opportunities will be evaluated for profitability, and much of that evaluation will be done with software developed specifically for the solar industry. Projects that pass internal screens may be pushed to the next phase of project development. Every company has its own internal processes for evaluating project feasibility, but several metrics are consistently used: Net System Yield (NSY), Levelized Cost of Electricity (LCOE) and Internal Rate of Return (IRR).

Each of these metrics offers a different view to compare projects, as well as evaluate the impact of isolated design or component choices within single projects. But these three metrics are not directly correlated, and seeking to optimize a system for one will likely yield a different result than optimizing for another. Understanding the uses and limitations of each can help solar professionals parse the many opportunities and design choices on their plates, and implementing end-to-end solar software platforms can be beneficial. Let’s explore the complexities of design optimization with respect to each of these factors, and then find out how solar software can help.

NSY: Net System Yield

NSY optimization has the goal of maximizing the AC energy, in kWh, that a solar plant can generate for the amount of DC nameplate capacity (in other words, solar panels) deployed within a certain usable site area. Because costs are not a factor, this tends to favor higher efficiency technologies such as tracking systems and higher efficiency solar panels and inverters. Similarly, higher reliability designs (such as underground collection circuits as opposed to overhead) also increase NSY to the extent that they can be quantified. Lower ground coverage ratios tend to improve NSY because they reduce inter-row shading, although the increased wire losses that result will help keep this in check to some degree. Optimal DC:AC ratios will be less than or equal to 1.0 to eliminate any DC capacity lost through inverter clipping. The logic is simple: Gain the highest solar harvest efficiency at any cost.

As may be obvious, making design decisions purely on the basis of optimizing NSY is rarely a good choice. Still, if reasonable boundary assumptions are set, NSY optimization can be a useful way of comparing the effect of different technologies purely from an efficiency standpoint or for comparing multiple sites purely on the basis of energy yield potential.

For example, let’s assume that a developer wants to compare the energy yield potential of two sites. Site A has higher solar insolation and relatively higher soiling losses, while Site B has lower solar insolation and relatively lower soiling losses. Because both sites may be at different latitudes, directly modeling the exact same fixed tilt system on both sites may yield biased results because module tilt would be the same. However, by allowing module tilt to “float” to the optimal angle that maximizes NSY, the yield potential of both sites can be more directly compared. Of course, NSY optimizations ignore an extremely important factor: cost. But in certain situations, that may be desired. Note that Net Capacity Factor (NCF) is closely related to NSY. In fact, these two metrics differ only by a constant (8,760 hrs./yr.) and therefore optimizing for NSY is equivalent to optimizing for NCF.

LCOE: Levelized Cost of Electricity

LCOE optimization has overall cost minimization per kWh generated as its central goal. It’s often used to compare the cost of generation of PV plants and to benchmark against other forms of electricity generation. LCOE fundamentally takes into account the total lifecycle costs of a plant (initial costs such as cost of capital, land, development, interconnection equipment and installation, and variable costs such as Operation and Maintenance, or O&M), and divides it by the AC electricity generation produced over the life of that plant.

As costs come into play, Balance of System (BoS) technologies with higher procurement, install or O&M costs become weighted, which allows for more effective comparisons to be drawn with lower cost technologies on a cost versus yield basis. Similarly, with LCOE optimization, row spacing tightens to form ideal packing densities that balance row-to-row shading losses with wire losses and costs.

LCOE optimizations tend to drive DC:AC ratios higher than 1.0 since costs, especially fixed costs, are included in the equation. This occurs because the marginal cost of adding DC capacity is lower than the relative incremental energy produced by the added DC capacity. The net effect is that LCOE decreases even though the kWh generated per module (or NSY) has also decreased. This may sometimes seem counter-intuitive, but fixed system costs play an important role in LCOE system optimization (or any optimization that includes total system cost).

Categorizing fixed costs as “sunk,” and therefore not relevant to design choices, can lead to less-than-optimal system configurations. To illustrate, let’s consider a scenario in which a developer determined that the LCOE for a particular site and system design will be sufficiently low enough below the PPA rate to achieve comfortable margins based on the best estimate of costs available at the time. However, as utility interconnection studies progress, it becomes apparent that greater utility infrastructure upgrades will be necessary to accommodate the new generation and therefore interconnection costs will increase significantly from those previously assumed. This negatively affects project economics. But should the developer accept these impacts to LCOE as reality or would a system re-design help to marginalize the additional interconnection fixed costs? The answer would likely be the latter. Redesigning the system to minimize LCOE in light of the higher interconnection fixed costs will likely lead to a higher DC:AC ratio (among other design changes) that will help to lower LCOE, albeit not the original cost. Having a fundamental understanding of this relationship can help guide developers when choosing whether to devote resources to investigating alternate design options or alternate sites.

IRR: Internal Rate of Return

Neither of the above metrics take into account Power Purchase Agreement (PPA) revenue or the ability to financially engineer at the project level. Enter IRR optimization for project developers seeking to assess the maximum financial yield from a given solar investment.

IRR optimization maximizes the relative benefit of system design choices based on the financial gain per unit of cost required. System optimization for two identical sites (same size, weather, geotech, fixed costs, etc.) with different PPA rates should therefore result in two different system designs when the optimization goal is to maximize IRR.

Higher PPA rates, or those with high escalation rates favor increased capacity over decreased system costs since the revenue gained from the additional capacity can outweigh the cost to generate it. This is where the increased energy yield from tracking systems can outweigh the additional cost to install and maintain them. Similarly, paying a premium for a higher efficiency substation transformer may be worthwhile even though it actually increases LCOE.

Time-of-use factors in a PPA can also yield system design changes that favor energy production during more profitable time periods by literally pointing the arrays “at the money,” even though overall energy yield may decrease. Compare these to LCOE minimized designs that completely ignore revenue and it is easy to see the benefit of IRR system optimization. But the use of IRR as a metric is not without its own limitations.

Because PPA rates impact IRR and vary from system to system, comparing multiple system designs only in terms of IRR can lead to biased conclusions. This may help to explain why LCOE is so readily used in the solar industry and why it may be a better metric for gauging the impact of a particular technology on a level playing field, or why it’s a better metric for benchmarking solar against other forms of generation.

How solar software can help

The sheer number of potential sites, equipment types, installation methods, labor rates, land and interconnection costs and design choices (tilts and row spacings, DC:AC ratios, BoS architectures, etc.) yield a nearly impossible set of scenarios to solve by hand. This is made even more difficult by the fact that site data and project costs trickle in as due diligence information is collected.

Developers and engineers are faced with an unending cycle of estimate revising and re-design as site information becomes available, costs solidify and new technologies hit the market. These challenges are typically overcome by holding many design parameters constant and choosing a limited set of variables and permutations to evaluate in detail. Time and resources often prevent true optimization or re-optimization in light of project changes.

Today, new software applications such as HST Inside allow for a new era of transparency in system design and financial modeling, as they can use computing power to design, simulate, estimate and optimize systems over an enormous number of variables. Many software tools already exist today, but most are heavily biased to residential and commercial rooftop systems. Design considerations with respect to ground mount racking structures and high voltage systems, for example, are almost non-existent within these tools. And while useful in terms of estimating energy yield for a known site design, software that relies on user input to define system configuration ultimately requires the user to determine system design on their own and fails to truly optimize.

Creating fully end-to-end platforms can empower investors, engineers and developers alike to quickly triage the most profitable and attractive projects and build a financeable portfolio that can be sold to IPPs or held for their cash flows. Today’s system designers need to be positioned to take on the deluge of utility-scale projects coming their way over the next six years. Other industries that apply computational algorithms to enhance financial value yield significant increases in engineering efficiencies as the cost of computations fall. Utility-scale solar design is ripe for this next wave of innovation.